AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Parametric study of EEG sensitivity to phase noise during face processing

<b>Background: </b> The present paper examines the visual processing speed of complex objects, here faces, by mapping the relationship between object physical properties and single-trial brain responses. Measuring visual processing speed is challenging because uncontrolled physical differences that co-vary with object categories might affect brain measurements, thus biasing our speed estimates. Recently, we demonstrated that early event-related potential (ERP) differences between faces and objects are preserved even when images differ only in phase information, and amplitude spectra are equated across image categories. Here, we use a parametric design to study how early ERP to faces are shaped by phase information. Subjects performed a two-alternative force choice discrimination between two faces (Experiment 1) or textures (two control experiments). All stimuli had the same amplitude spectrum and were presented at 11 phase noise levels, varying from 0% to 100% in 10% increments, using a linear phase interpolation technique. Single-trial ERP data from each subject were analysed using a multiple linear regression model. <b>Results: </b> Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces. <b>Conclusion: </b> Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses

Analytical maximum likelihood estimation of stellar magnetic fields

The polarised spectrum of stellar radiation encodes valuable information on the conditions of stellar atmospheres and the magnetic fields that permeate them. In this paper, we give explicit expressions to estimate the magnetic field vector and its associated error from the observed Stokes parameters. We study the solar case where specific intensities are observed and then the stellar case, where we receive the polarised flux. In this second case, we concentrate on the explicit expression for the case of a slow rotator with a dipolar magnetic field geometry. Moreover, we also give explicit formulae to retrieve the magnetic field vector from the LSD profiles without assuming mean values for the LSD artificial spectral line. The formulae have been obtained assuming that the spectral lines can be described in the weak field regime and using a maximum likelihood approach. The errors are recovered by means of the hermitian matrix. The bias of the estimators are analysed in depth.Comment: accepted for publication in MNRA

Evolution of the Most Massive Galaxies to z=0.6: I. A New Method for Physical Parameter Estimation

We use principal component analysis (PCA) to estimate stellar masses, mean stellar ages, star formation histories (SFHs), dust extinctions and stellar velocity dispersions for ~290,000 galaxies with stellar masses greater than $10^{11}Msun and redshifts in the range 0.4<z<0.7 from the Baryon Oscillation Spectroscopic Survey (BOSS). We find the fraction of galaxies with active star formation first declines with increasing stellar mass, but then flattens above a stellar mass of 10^{11.5}Msun at z~0.6. This is in striking contrast to z~0.1, where the fraction of galaxies with active star formation declines monotonically with stellar mass. At stellar masses of 10^{12}Msun, therefore, the evolution in the fraction of star-forming galaxies from z~0.6 to the present-day reaches a factor of ~10. When we stack the spectra of the most massive, star-forming galaxies at z~0.6, we find that half of their [OIII] emission is produced by AGNs. The black holes in these galaxies are accreting on average at ~0.01 the Eddington rate. To obtain these results, we use the stellar population synthesis models of Bruzual & Charlot (2003) to generate a library of model spectra with a broad range of SFHs, metallicities, dust extinctions and stellar velocity dispersions. The PCA is run on this library to identify its principal components over the rest-frame wavelength range 3700-5500A. We demonstrate that linear combinations of these components can recover information equivalent to traditional spectral indices such as the 4000A break strength and HdA, with greatly improved S/N. This method is able to recover physical parameters such as stellar mass-to-light ratio, mean stellar age, velocity dispersion and dust extinction from the relatively low S/N BOSS spectra. We examine the sensitivity of our stellar mass estimates to the input parameters in our model library and the different stellar population synthesis models.Comment: 20 pages, 18 Figures, submitted to MNRA